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Asymmetric stochastic volatility models: properties and particle filter-based simulated maximum likelihood estimation

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dc.affiliation.dptoUC3M. Departamento de EstadĂ­sticaes
dc.contributor.authorMao, Xiuping
dc.contributor.authorCzellar, Veronika
dc.contributor.authorRuiz Ortega, Esther
dc.contributor.authorLopes Moreira Da Veiga, MarĂ­a Helena
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2022-06-01T15:09:29Z
dc.date.available2022-06-01T15:09:29Z
dc.date.issued2020-01-01
dc.description.abstractThe statistical properties of a general family of asymmetric stochastic volatility (A-SV)models which capture the leverage effect in financial returns are derived providing analyt- ical expressions of moments and autocorrelations of power-transformed absolute returns.The parameters of the A-SV model are estimated by a particle filter-based simulated max- imum likelihood estimator and Monte Carlo simulations are carried out to validate it. Itis shown empirically that standard SV models may significantly underestimate the value- at-risk of weekly S&P 500 returns at dates following negative returns and overestimate itafter positive returns. By contrast, the general specification proposed provide reliable fore- casts at all dates. Furthermore, based on daily S&P 500 returns, it is shown that the mostadequate specification of the asymmetry can change over time.en
dc.description.sponsorshipWe gratefully acknowledge the financial support from the Spanish Government, contract grants ECO2015-70331-C2-2-R and ECO2015-65701-P (MINECO/FEDER), the computer support from EUROFIDAI, and the FCT grant UID/GES/00315/2013.en
dc.identifier.bibliographicCitationMao, X., Czellar, V., Ruiz, E., & Veiga, H. (2020). Asymmetric stochastic volatility models: Properties and particle filter-based simulated maximum likelihood estimation. Econometrics and Statistics, 13, pp. 84-105.es
dc.identifier.doihttps://doi.org/10.1016/j.ecosta.2019.08.002
dc.identifier.issn2452-3062
dc.identifier.publicationfirstpage84es
dc.identifier.publicationlastpage105es
dc.identifier.publicationtitleEconometrics and Statisticsen
dc.identifier.publicationvolume13es
dc.identifier.urihttps://hdl.handle.net/10016/34968
dc.identifier.uxxiAR/0000025524
dc.language.isoenges
dc.publisherElsevieres
dc.relation.projectIDGobierno de España. ECO2015-70331-C2-2-Res
dc.relation.projectIDGobierno de España. ECO2015-65701-Pes
dc.rights© 2019 EcoSta Econometrics and Statistics. Published by Elsevier B.V. All rights reserved.en
dc.rightsAtribuciĂ³n-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaEstadĂ­sticaes
dc.subject.otherLeverage effecten
dc.subject.otherParticle filteringen
dc.subject.otherSV modelsen
dc.subject.otherValue-at-risken
dc.titleAsymmetric stochastic volatility models: properties and particle filter-based simulated maximum likelihood estimationen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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